Forecasting Biases & Decision Distortions

Primes about systematic distortions in predicting, planning, and deciding under uncertainty, including optimism and planning-fallacy biases, a default bias toward adding rather than removing, self-defeating predictions, risk that nobody owns, and the premium paid to verify before acting.

9 primes in this family — primes that sit near one another in abstraction space (k-means over structural-signature embeddings). Each is shown with its short description.

• Additive Bias — When asked to improve a system, agents reliably reach first for adding a component and underweight removing one, so the search distribution over transformations is direction-asymmetric and long-lived systems accrete past their purpose-optimal complexity.
• Change Notification — Advance warning, directed at those who depend on a system, that it is about to change in a way they need lead time to prepare for.
• Cromwell's Rule — Never assign prior probability of exactly 0 or 1 to a contingent proposition, because multiplicative Bayesian updating can never move a belief away from those endpoints.
• Optimism Bias — Overestimate positive outcomes.
• Planning Fallacy — Forecasters of a novel task default to inside-view simulation over the outside-view reference class, systematically underestimating time, cost, and risk.
• Regret — Disvalue from comparing an outcome against a better forgone alternative.
• Self-Defeating Prediction — Belief in a forecast moves conditions against it.
• Uncertainty-Driven Verification Premium — Under uncertainty, agents pay to retreat from unverified options toward verified ones.
• Unowned Known Risk — A hazard that is common knowledge yet reliably un-acted-upon because prevention costs are diffusely owned, horizons mismatch, precursors are normalised, and the outcome is later recast as a surprise.